Expand brief aliases into comprehensive code review and testing prompts.
npx skills add https://github.com/JGalego/Bag-of-Tricks --skill steno-jgalego
Please help me install this Agent Skill. Skill: steno Source: https://github.com/JGalego/Bag-of-Tricks/tree/main/steno Command: npx skills add https://github.com/JGalego/Bag-of-Tricks --skill steno-jgalego
The steno Skill tackles the problem of repetitive and lengthy code review and testing prompts. It provides quick and concise ways to generate comprehensive and context-specific prompts, saving time and enhancing productivity for developers.
Run steno r app.py to review the code in 'app.py' and automatically send the prompt to your chosen LLM for a detailed review.
High-intent search queries and answers about installing and using this skill.
Code review prompts are generated by expanding brief alias commands into comprehensive, context-specific instructions. The steno Skill splices file content directly into the prompt, creating review-ready output for LLMs without manual typing.
Automated prompt generation for Python testing uses concise custom aliases that expand into detailed development prompts. These expanded prompts integrate directly with LLM APIs to provide immediate, context-specific testing feedback.
Yes, prompt expansion for code review requires a Python environment and API access to your selected LLM provider. The steno Skill operates within programming environments to enhance the efficiency and accuracy of LLM interactions.
Yes, custom aliases can splice file content directly into LLM prompts. The steno Skill supports adding personal shortcuts for common development tasks, automatically injecting file-specific context into the expanded prompts.
To run an expanded prompt through an LLM, execute a command like 'steno r <filename>'. This expands the alias, splices the file content into the prompt, and sends it to your chosen LLM for detailed code review feedback.
The limitation of brief alias prompt generation is its strict dependency on a Python environment and external LLM API access. Without these prerequisites configured, the aliases cannot expand or generate the necessary review and testing prompts.
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